Oxygen & Voltage Gradients

Introduction

This rmd. file imports oxygen and voltage data from Google sheet Data is used to plot a oxygen and voltage gradient map of the winogradsky column Exponential and linear models were used to analyze data

Loading Packages

library(tidyverse) 

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✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
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✔ purrr     1.0.2     ── Conflicts ──────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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✖ dplyr::lag()    masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors

library(googledrive) 
library(googlesheets4)

Attaching package: ‘googlesheets4’

The following objects are masked from ‘package:googledrive’:

    request_generate, request_make

#library(ggplot2)
library(knitr)
library(minpack.lm)
library(broom)
library(purrr)
library(lme4)

Loading required package: Matrix

Attaching package: ‘Matrix’

The following objects are masked from ‘package:tidyr’:

    expand, pack, unpack

Importing from google sheet

googlesheets4::gs4_deauth()

CleanData_Oxygen <- googlesheets4::read_sheet("https://docs.google.com/spreadsheets/d/1hNakCyqPsTNRFpf1lGjoBQ-papyZ6xHKT6mcvh3LMb8/edit?pli=1&gid=842906991#gid=842906991", "CleanData_Oxygen") |>
  mutate(Sample = as.character(Sample))

✖ Request failed [429]. Retry 1 happens in 5.1 seconds ...
✔ Reading from Microcosms.
✔ Range ''CleanData_Oxygen''.

kable(CleanData_Oxygen) 

Registered S3 methods overwritten by 'htmltools':
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  print.paged_df     

Sample	Depth_cm	DO_µg_L	temp_C
1004	0.0	276.0	21.2
1004	6.2	276.0	21.2
1004	8.4	275.0	21.2
1004	13.1	6.3	21.2
1004	22.0	3.4	21.2
1003	0.0	101.0	20.0
1003	5.8	62.0	20.0
1003	8.4	52.0	20.0
1003	13.1	29.0	20.0
1003	21.3	21.0	20.0
1002	0.0	80.0	19.8
1002	6.6	45.0	19.8
1002	9.3	26.0	19.8
1002	15.1	21.0	19.8
1002	21.9	19.0	19.8
1001_C	0.0	190.0	19.0
1001_C	6.9	106.0	19.0
1001_C	10.8	99.0	19.0
1001_C	16.6	57.0	19.0
1001_C	22.7	25.0	19.0
1001_W	0.0	103.0	19.0
1001_W	6.7	34.0	19.0
1001_W	10.2	21.0	19.0
1001_W	15.2	11.0	19.0
1001_W	22.8	8.0	19.0

googlesheets4::gs4_deauth()

CleanData_Voltage <- googlesheets4::read_sheet("https://docs.google.com/spreadsheets/d/1hNakCyqPsTNRFpf1lGjoBQ-papyZ6xHKT6mcvh3LMb8/edit?pli=1&gid=842906991#gid=842906991", "CleanData_Voltage") |>
  mutate(Sample = as.character(Sample))

✔ Reading from Microcosms.
✔ Range ''CleanData_Voltage''.

kable(CleanData_Voltage)

Sample	Depth_cm	Voltage_Water_10s	Voltage_Water_Max	Voltage_Tap_10s	Voltage_Tap_Max
1004	0.0	NA	NA	121.0	121.0
1004	6.2	160.2	190	-168.0	-168.0
1004	8.4	74.4	79	84.0	122.0
1004	13.1	-55.0	58	43.0	51.0
1004	22.0	-200.0	204	84.0	102.0
1003	0.0	NA	NA	74.0	300.0
1003	5.8	27.0	27	-13.0	26.0
1003	8.4	59.0	83	57.0	57.0
1003	13.1	-20.0	101	-94.0	-136.0
1003	21.3	-25.0	-30	161.0	177.0
1002	0.0	NA	NA	51.0	199.0
1002	6.6	-39.0	-40	13.0	30.0
1002	9.3	-4.3	30	196.0	199.0
1002	15.1	24.0	-4	16.0	25.0
1002	21.9	27.0	28	194.0	199.0
1001_C	0.0	NA	NA	187.0	187.0
1001_C	6.9	-10.0	-22	76.0	300.0
1001_C	10.8	25.0	60	-2.8	-2.8
1001_C	16.6	-6.0	-23	-10.5	-3.0
1001_C	22.7	6.0	48	168.0	174.0
1001_W	0.0	NA	NA	187.0	187.0
1001_W	6.7	16.0	-9	13.0	170.0
1001_W	10.2	31.0	33	-48.0	-48.0
1001_W	15.2	34.0	40	24.0	24.0
1001_W	22.8	40.0	-17	72.0	72.0

Combine O2 & Voltage data by sample & depth

Gradients <- left_join(x = CleanData_Oxygen, y = CleanData_Voltage, by = c("Sample", "Depth_cm"))

Oxygen Gradient Plot and Modelling

ggplot(data = CleanData_Oxygen) +
geom_point(aes(y = DO_µg_L, x = Depth_cm)) + 
  #scale_y_reverse() +
  #labs( title= "Oxygen Gradients", caption= "Figure X. Dissolved Oxygen (DO) (µg/L) measured in four Winogradsky colums at 5 depths (cm)") 
  #+ theme(plot.caption= element_text(size = 11, hjust=0)) +
  #geom_path(aes(y = Depth_cm, x = DO_µg_L)) +
  facet_grid(cols = vars(Sample)) +
  theme_bw()

#define exponential decay function for data fitting.
exp_decay <- function(x, i, mu){y = i * exp(mu * x)}


O2_nest <- CleanData_Oxygen |>  #alternate forward pipe is %>% loaded with tidyverse
  nest(.by = "Sample") |>
  mutate(DecayFit = purrr::map(data, ~nlsLM(DO_µg_L ~ exp_decay(x = Depth_cm, i, mu),
                                            data = .x)),
         DecayTidy = purrr::map(DecayFit, tidy),
         DecayParam = purrr::map(DecayFit, glance),
         DecayPredict = purrr::map(DecayFit, augment)
         )

Warning: There were 5 warnings in `mutate()`.
The first warning was:
ℹ In argument: `DecayFit = purrr::map(...)`.
Caused by warning in `nlsLM()`:
! No starting values specified for some parameters.
Initializing ‘i’, ‘mu’ to '1.'.
Consider specifying 'start' or using a selfStart model
ℹ Run ]8;;ide:run:dplyr::last_dplyr_warnings()dplyr::last_dplyr_warnings()]8;; to see the 4 remaining warnings.

O2_nest |>
unnest(cols = c(DecayTidy)) |>
 select(-c(data, DecayFit, DecayParam, DecayPredict)) |>
  select(-c(statistic)) |>
  pivot_wider(id_cols = Sample, names_from = term, values_from = c(estimate, std.error, p.value)) |>
  kable()

Sample	estimate_i	estimate_mu	std.error_i	std.error_mu	p.value_i	p.value_mu
1004	326.62310	-0.0779518	101.553081	0.0494649	0.0487222	0.2131346
1003	100.85694	-0.0833277	3.557277	0.0059295	0.0000963	0.0007804
1002	78.61257	-0.0897172	6.441339	0.0139884	0.0011845	0.0076805
1001_C	190.11905	-0.0743515	10.391455	0.0078457	0.0003563	0.0024909
1001_W	102.49196	-0.1553412	3.251191	0.0097456	0.0000701	0.0005369

Voltage Gradient Plot and Linear Model

ggplot(data = Gradients) +
  geom_point(aes(x = Depth_cm, y = Voltage_Water_10s)) + 
  facet_grid(cols = vars(Sample)) + 
  theme_bw()

  Linear_fit <- Gradients %>% 
  group_by(Sample) %>%
  group_modify(~ {

  linear <- lm(Voltage_Water_10s ~ Depth_cm, data = .x, na.action = na.exclude)
  augment(linear, data = .x)}) %>%
  ungroup()


  Linear_fit |>   
    ggplot(aes(x = Depth_cm, y = Voltage_Water_10s)) +
    geom_point() +
    geom_line(aes(y = .fitted)) +
    #Showing residuals 
    geom_segment(aes(xend = Depth_cm, yend = .fitted), color = "red") +
    facet_grid(cols = vars((Sample))) +
    theme_bw()

NA
NA

 
Linear_fit_param <- Linear_fit |>
  group_by(Sample) |>
  group_modify(~ {
    linear_model <- lm(Voltage_Water_10s ~ Depth_cm, data = .x)
    tidy_model <- tidy(linear_model)  
    tidy_model$Sample <- unique(.x$Sample)  # Ensure Sample column is added
    return(tidy_model)  
  }) |>
  ungroup() 

Warning: Unknown or uninitialised column: `Sample`.Warning: Unknown or uninitialised column: `Sample`.Warning: Unknown or uninitialised column: `Sample`.Warning: Unknown or uninitialised column: `Sample`.Warning: Unknown or uninitialised column: `Sample`.

Linear_fit_param <- Linear_fit_param |>
  mutate(term = str_replace(term, "\\(Intercept\\)", "I"))

Linear_fit_param |>
  select(-c(statistic)) |>
  pivot_wider(id_cols = Sample, names_from = term, values_from = c(estimate, std.error, p.value)) |>
  kable()

Sample	estimate_I	estimate_Depth_cm	std.error_I	std.error_Depth_cm	p.value_I	p.value_Depth_cm
1001_C	1.370844	0.1669583	24.814453	1.6058696	0.9609665	0.9266817
1001_W	12.287870	1.3087162	6.912976	0.4609137	0.2174605	0.1048823
1002	-51.186449	4.0159886	21.382850	1.4776076	0.1390247	0.1129043
1003	66.227753	-4.6072225	34.968252	2.5893530	0.1987344	0.2171558
1004	269.340131	-22.0877369	39.168813	2.8330251	0.0205004	0.0160561

NA

Can only import one image at a time right now, we could choose some notable ones to display?